Algorithms and data structures Books

Book SynopsisTable of ContentsPart I: Rules of the Game 1. Introducing Network Algorithmics 2. Network Implementation Models 3. Fifteen Implementation Principles 4. Principles in Action Part II: Playing with Endnodes 5. Copying Data 6. Transferring Control 7. Maintaining Timers 8. Demultiplexing 9. Protocol Processing Part III: Playing with Routers 10. Exact-Match Lookups 11. Prefix-Match Lookups 12. Packet Classification 13. Switching 14. Scheduling Packets 15. Routers as Distributed Systems Part IV: Endgame 16. Measuring Network Traffic 17. Network Security 18. Conclusions Appendix: Detailed Models

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Book SynopsisThe conventional wisdom was that biology influenced mathematics and computer science. But a new approach has taken hold: that of transferring methods and tools from computer science to biology. The reverse trend is evident in Grammars and Automata for String Processing: From Mathematics and Computer Science to Biology and Back. The contributors address the structural (syntactical) view of the domain. Mathematical linguistics and computer science can offer various tools for modeling complex macromolecules and for analyzing and simulating biological issues. This collection is valuable for students and researchers in biology, computer science, and applied mathematics.Table of ContentsLogistics, Languages and Combinatorics. Models of Molecular Computing.

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Book SynopsisRandomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the ChernoffâHoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as ChernoffâHoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus makingTrade ReviewReview of the hardback: 'It is beautifully written, contains all the major concentration results, and is a must to have on your desk.' Richard LiptonReview of the hardback: 'Concentration bounds are at the core of probabilistic analysis of algorithms. This excellent text provides a comprehensive treatment of this important subject, ranging from the very basic to the more advanced tools, including some recent developments in this area. The presentation is clear and includes numerous examples, demonstrating applications of the bounds in analysis of algorithms. This book is a valuable resource for both researchers and students in the field.' Eli Upfal, Brown UniversityReview of the hardback: 'Concentration inequalities are an essential tool for the analysis of algorithms in any probabilistic setting. There have been many recent developments on this subject, and this excellent text brings them together in a highly accessible form.' Alan Frieze, Carnegie Mellon UniversityReview of the hardback: 'The book does a superb job of describing a collection of powerful methodologies in a unified manner; what is even more striking is that basic combinatorial and probabilistic language is used in bringing out the power of such approaches. To summarize, the book has done a great job of synthesizing diverse and important material in a very accessible manner. Any student, researcher, or practitioner of computer science, electrical engineering, mathematics, operations research, and related fields, could benefit from this wonderful book. The book would also make for fruitful classes at the undergraduate and graduate levels. I highly recommend it.' Aravind Srinivasan, SIGACT NewsReview of the hardback: '… the strength of this book is that it is appropriate for both the beginner as well as the experienced researcher in the field of randomized algorithms … The exposition style […] combines informal discussion with formal definitions and proofs, giving first the intuition and motivation for the probabalistic technique at hand. … I highly recommend this book both as an advanced as well as an introductory textbook, which can also serve the needs of an experienced researcher in algorithmics.' Yannis C. Stamatiou, Mathematical ReviewsReviews of the hardback: 'This timely book brings together in a comprehensive and accessible form a sophisticated toolkit of powerful techniques for the analysis of randomized algorithms, illustrating their use with a wide array of insightful examples. This book is an invaluable resource for people venturing into this exciting field of contemporary computer science research.' Prabhakar Ragahavan, Yahoo ResearchTable of Contents1. Chernoff–Hoeffding bounds; 2. Applying the CH-bounds; 3. CH-bounds with dependencies; 4. Interlude: probabilistic recurrences; 5. Martingales and the MOBD; 6. The MOBD in action; 7. Averaged bounded difference; 8. The method of bounded variances; 9. Interlude: the infamous upper tail; 10. Isoperimetric inequalities and concentration; 11. Talagrand inequality; 12. Transportation cost and concentration; 13. Transportation cost and Talagrand's inequality; 14. Log–Sobolev inequalities; Appendix A. Summary of the most useful bounds.

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Book SynopsisExplores the Impact of the Analysis of Algorithms on Many Areas within and beyond Computer ScienceA flexible, interactive teaching format enhanced by a large selection of examples and exercisesDeveloped from the author's own graduate-level course, Methods in Algorithmic Analysis presents numerous theories, techniques, and methods used for analyzing algorithms. It exposes students to mathematical techniques and methods that are practical and relevant to theoretical aspects of computer science.After introducing basic mathematical and combinatorial methods, the text focuses on various aspects of probability, including finite sets, random variables, distributions, Bayes' theorem, and Chebyshev inequality. It explores the role of recurrences in computer science, numerical analysis, engineering, and discrete mathematics applications. The author then describes the powerful tool of generating functions, which is demonstrated in enumeTrade Review…helpful to any mathematics student who wishes to acquire a background in classical probability and analysis … This is a remarkably beautiful book that would be a pleasure for a student to read, or for a teacher to make into a year's course.—Harvey Cohn, Computing Reviews, May 2010Table of ContentsPreliminaries. Combinatorics. Probability. More about Probability. Recurrences or Difference Equations. Introduction to Generating Functions. Enumeration with Generating Functions. Further Enumeration Methods. Combinatorics of Strings. Introduction to Asymptotics. Asymptotics and Generating Functions. Review of Analytic Techniques. Appendices. Bibliography. Answers/Hints to Selected Problems. Index.

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Table of ContentsA table of contents is not available for this title.

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Book SynopsisEnergy-Aware Memory Management for Embedded Multimedia Systems: A Computer-Aided Design Approach presents recent computer-aided design (CAD) ideas that address memory management tasks, particularly the optimization of energy consumption in the memory subsystem. It explains how to efficiently implement CAD solutions, including theoretical methods and novel algorithms. The book covers various energy-aware design techniques, including data-dependence analysis techniques, memory size estimation methods, extensions of mapping approaches, and memory banking approaches. It shows how these techniques are used to evaluate the data storage of an application, reduce dynamic and static energy consumption, design energy-efficient address generation units, and much more.Providing an algebraic framework for memory management tasks, this book illustrates how to optimize energy consumption in memory subsystems using CAD solutions. The algorithmic style ofTable of ContentsComputer-Aided Design for the Energy Optimization in the Memory Architecture of Embedded Systems. The Power of Polyhedra. Computation of Data Storage Requirements for Affine Algorithmic Specifications. Polyhedral Techniques for Parametric Memory Requirement Estimation. Storage Allocation for Streaming-Based Register File. Optimization of the Dynamic Energy Consumption and Signal Mapping in Hierarchical Memory Organizations. Leakage Current Mechanisms and Estimation in Memories and Logic. Leakage Control in SoCs. Energy-Efficient Memory Port Assignment. Energy-Efficient Address-Generation Units and Their Design Methodology. Index.

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Book SynopsisProbability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in nature. Probability theory and Bayesian computing together provide an alternative framework to deal with incomplete and uncertain data. Decision-Making Tools and Methods for Incomplete and Uncertain DataEmphasizing probability as an alternative to Boolean logic, Bayesian Programming covers new methods to build probabilistic programs for real-world applications. Written by the team who designed and implemented an efficient probabilistic inference engine to interpret Bayesian programs, the book offers many Python examples that are also available on a supplementary website together with an interpreTrade Review"Bayesian Programming comprises a methodology, a programming language, and a set of tools for developing and applying … complex models. … The approach is described in great detail, with many worked examples backed up by an online code repository. Unlike other books that tend to focus almost entirely on mathematics, this one gives equal time to conceptual and methodological guidance for the model-builder. It grapples with the knotty problems that arise in practice, some of which do not yet have clear solutions."—From the Foreword by Stuart Russell, University of California, Berkeley"The book has many worked examples backed up by an online code repository. The book provides a contibution on conceptual and methodological guidelines for model-builders. The authors discuss the problem how to build a Bayesian computer. The book has an excellent bibliography."—Nirode C. Mohanty, in Zentralblatt MATH 1281 Table of ContentsIntroduction. Bayesian Programming Principles: Basic Concepts. Incompleteness and Uncertainty. Description = Specification + Identification. The Importance of Conditional Independence. Bayesian Program = Description + Question. Bayesian Programming Cookbook: Information Fusion. Bayesian Programming with Coherence Variables. Bayesian Programming Subroutines. Bayesian Programming Conditional Statement. Bayesian Programming Iteration. Bayesian Programming Formalism and Algorithms: Bayesian Programming Formalism. Bayesian Models Revisited. Bayesian Inference Algorithms Revisited. Bayesian Learning Revisited. Frequently Asked Questions and Frequently Argued Matter: Frequently Asked Question and Frequently Argued Matter. Glossary. Index.

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Book SynopsisSince the start of modern computing, the studies of living organisms have inspired the progress in developing computers and intelligent machines. In particular, the methods of search and foraging are the benchmark problems for robotics and multi-agent systems. The highly developed theory of search and screening involves optimal search plans that are obtained by standard optimization techniques while the foraging theory addresses search plans that mimic the behavior of living foragers.Search and Foraging: Individual Motion and Swarm Dynamics examines how to program artificial search agents so that they demonstrate the same behavior as predicted by the foraging theory for living organisms. For cybernetics, this approach yields techniques that enable the best online search planning in varying environments. For biology, it allows reasonable insights regarding the internal activity of living organisms performing foraging tasks.The book discusses foraTrade Review"The book is valuable reading both for teaching inspiration as well as for research insights into optimization, modeling, mathematical biology, and robot programming."—Zentralblatt MATH 1327Table of ContentsIntroduction. Methods of Optimal Search and Screening. Methods of Optimal Foraging. Models of Individual Search and Foraging. Coalitional Search and Swarm Dynamics. Remarks on Swarm Robotic Systems for Search and Foraging. Conclusion. Bibliography. Index.

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Book SynopsisDESCRIPTION Any large application needs an efficient way to handle the constant messages passing between components in the system. Billed as "messaging that just works," the RabbitMQ message broker initially appeals to developers because it's lightweight, easy to set up, and low maintenance. They stick with it because it's powerful, fast, and up to nearly anything that can be thrown at it. This book takes readers beyond the basics and explores the challenges of clustering and distributing messages across enterprise-level data-centers using RabbitMQ. RabbitMQ in Depth is a practical guide to building and maintaining message-based systems. This book covers detailed architectural and operational use of RabbitMQ with an emphasis on not just how it works but why it works the way it does. It provides examples and detailed explanations of everything from low-level communication to integration with third-party systems. It also offers insights needed to make core architectural choices and develop procedures for effective operational management. KEY FEATURES Approachable detailed resource Explains the "how" and "why" of RabbitMQ Takes readers well beyond the basics AUDIENCE Written for programmers with a basic understanding of messaging oriented systems and RabbitMQ. ABOUT THE TECHNOLOGY RabbitMQ is an open-source message broker software that programs can use to exchange messages with each other to create scalable and reliable application architectures.

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Book SynopsisThe big challenge for a successful AI project isn’t deciding which problems you can solve. It’s deciding which problems you should solve. In Managing Successful AI Projects, author and AI consultant Veljko Krunic reveals secrets for succeeding in AI that he developed with Fortune 500 companies, early-stage start-ups, and other business across multiple industries. Key Features · Selecting the right AI project to meet specific business goals · Economizing resources to deliver the best value for money · How to measure the success of your AI efforts in the business terms · Predict if you are you on the right track to deliver your intended business results For executives, managers, team leaders, and business-focused data scientists. No specific technical knowledge or programming skills required. About the technology Companies small and large are initiating AI projects, investing vast sums of money on software, developers, and data scientists. Too often, these AI projects focus on technology at the expense of actionable or tangible business results, resulting in scattershot results and wasted investment. Managing Successful AI Projects sets out a blueprint for AI projects to ensure they are predictable, successful, and profitable. It’s filled with practical techniques for running data science programs that ensure they’re cost effective and focused on the right business goals. Veljko Krunic is an independent data science consultant who has worked with companies that range from start-ups to Fortune 10 enterprises. He holds a PhD in Computer Science and an MS in Engineering Management, both from the University of Colorado at Boulder. He is also a Six Sigma Master Black Belt.

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Book Synopsis

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Book Synopsis

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Book SynopsisAlgorithms are at the heart of every nontrivial computer application, and algorithmics is a modern and active area of computer science. Every computer scientist and every professional programmer should know about the basic algorithmic toolbox: structures that allow efficient organization and retrieval of data, frequently used algorithms, and basic techniques for modeling, understanding and solving algorithmic problems. This book is a concise introduction addressed to students and professionals familiar with programming and basic mathematical language. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, and optimization. The algorithms are presented in a modern way, with explicitly formulated invariants, and comment on recent trends such as algorithm engineering, memory hierarchies, algorithm libraries and certifying algorithms. The authors use pictures, words and high-level pseudocode to explain the algorithms, and then they present more detail on efficient implementations using real programming languages like C++ and Java. The authors have extensive experience teaching these subjects to undergraduates and graduates, and they offer a clear presentation, with examples, pictures, informal explanations, exercises, and some linkage to the real world. Most chapters have the same basic structure: a motivation for the problem, comments on the most important applications, and then simple solutions presented as informally as possible and as formally as necessary. For the more advanced issues, this approach leads to a more mathematical treatment, including some theorems and proofs. Finally, each chapter concludes with a section on further findings, providing views on the state of research, generalizations and advanced solutions.Trade Review"This is another mainstream textbook on algorithms and data structures, mainly intended for undergraduate students and professionals … . The two-layer index table is also detailed and helpful. I do enjoy reading the informative sections of historical notes and further findings at the end of each chapter. … This book is very well written, with the help of … clear figures and tables, as well as many interesting and inspiring examples." Zhizhang Shen, Zentralblatt MATH, Vol. 1146, 2008"... the book develops the basic fundamental principles underlying their design and analysis without sacrificing depth or rigor. The authors' insight, knowledge and active research on algorithms and data structures provide a very solid approach to the book. I particularly liked their "as informally as possible and as formally as necessary" writing style, and I enjoyed a lot their decision to not only discuss classical results, but to broaden the view to alternative implementations, memory hierarchies and libraries, which transmits novelty and increases interest...I think that this book will be a superb addition particularly useful for teachers of undergraduate courses, to graduate students in Computer Science, and to researchers that work, or intend to work, with algorithms." Jordi Petit, Computer Science Review 3, 2009 "Mehlhorn and Sanders write well, and the well-organized presentation reflects their experience and interest in the various topics... it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. [...]This text is intended for undergraduate computer science (CS) majors, and focuses on algorithm analysis. … it is an excellent reference, and could possibly be used in a transition course, serving students coming to graduate CS courses from other technical fields. Finally, the book contains interesting tidbits that are not readily available elsewhere." M. G. Murphy, ACM Computing Reviews, October 2008"A 'Toolbox' should be portable, practical, and useful. This book is all these, covering a nice swath of the classic CS algorithms but addressing them in a way that is accessible to the student and practitioner. Furthermore, it manages to incorporate interesting examples as well as subtle examples of wit compressed into its 300 pages. Although it is not tied to any one language or library, it provides practical references to efficient open-source implementations of many of the algorithms and data structures; these should be the first refuge of the commercial developer. I can easily recommend this book as an intermediate undergraduate text, a refresher for those of us who only dimly remember our intermediate undergraduate courses, and as a reference for the professional development craftsman." Hal C. Elrod, SIGACT News Book Review Column 42(4) 2011Table of ContentsAppetizer: Integer Arithmetics.- Representing Sequences by Arrays and Linked Lists.- Hash Tables and Associative Arrays.- Sorting and Selection.- Priority Queues.- Sorted Sequences.- Graph Representation.- Graph Traversal.- Shortest Paths.- Minimum Spanning Trees.- Generic Approaches to Optimization.

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Book SynopsisThis book constitutes the proceedings of the Third International Conference on Abstract State Machines, B, VDM, and Z, which took place in Pisa, Italy, in June 2012. The 20 full papers presented together with 2 invited talks and 13 short papers were carefully reviewed and selected from 59 submissions. The ABZ conference series is dedicated to the cross-fertilization of five related state-based and machine-based formal methods: Abstract State Machines (ASM), Alloy, B, VDM, and Z. They share a common conceptual foundation and are widely used in both academia and industry for the design and analysis of hardware and software systems. The main goal of this conference series is to contribute to the integration of these formal methods, clarifying their commonalities and differences to better understand how to combine different approaches for accomplishing the various tasks in modeling, experimental validation and mathematical verification of reliable high-quality hardware/software systems.

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Book SynopsisThe design and analysis of data structures and efficient algorithms has gained considerable importance in recent years. The concept of "algorithm" is central in computer science, and "efficiency" is central in the world of money. I have organized the material in three volumes and nine chapters. Vol. 1: Sorting and Searching (chapters I to III) Vol. 2: Graph Algorithms and NP-completeness (chapters IV to VI) Vol. 3: Multi-dimensional Searching and Computational G- metry (chapters VII and VIII) Volumes 2 and 3 have volume 1 as a common basis but are indepen­ dent from each other. Most of volumes 2 and 3 can be understood without knowing volume 1 in detail. A general kowledge of algorith­ mic principles as laid out in chapter 1 or in many other books on algorithms and data structures suffices for most parts of volumes 2 and 3. The specific prerequisites for volumes 2 and 3 are listed in the prefaces to these volumes. In all three volumes we present and analyse many important efficient algorithms for the fundamental computa­ tional problems in the area. Efficiency is measured by the running time on a realistic model of a computing machine which we present in chapter I. Most of the algorithms presented are very recent inven­ tions; after all computer science is a very young field. There are hardly any theorems in this book which are older than 20 years and at least fifty percent of the material is younger than 10 years.Table of ContentsI. Foundations.- 1. Machine Models: RAM and RASP.- 2. Randomized Computations.- 3. A High Level Programming Language.- 4. Structured Data Types.- 4.1 Queues and Stacks.- 4.2 Lists.- 4.3 Trees.- 5. Recursion.- 6. Order of Growth.- 7. Secondary Storage.- 8. Exercises.- 9. Bibliographic Notes.- II. Sorting.- 1. General Sorting Methods.- 1.1 Sorting by Selection, a First Attempt.- 1.2 Sorting by Selection: Heapsort.- 1.3 Sorting by Partitioning: Quicksort.- 1.4 Sorting by Merging.- 1.5 Comparing Different Algorithms.- 1.6 Lower Bounds.- 2. Sorting by Distribution.- 2.1 Sorting Words.- 2.2 Sorting Reals by Distribution.- 3. The Lower Bound on Sorting, Revisited.- 4. The Linear Median Algorithm.- 5. Exercises.- 6. Bibliographic Notes.- III. Sets.- 1. Digital Search Trees.- 1.1 Tries.- 1.2 Static Tries or Compressing Sparse Tables.- 2. Hashing.- 2.1 Hashing with Chaining.- 2.2 Hashing with Open Addressing.- 2.3 Perfect Hashing.- 2.4 Universal Hashing.- 2.5 Extendible Hashing.- 3. Searching Ordered Sets.- 3.1 Binary Search and Search Trees.- 3.2 Interpolation Search.- 4. Weighted Trees.- 4.1 Optimum Weighted Trees, Dynamic Programming, and Pattern Matching.- 4.2 Nearly Optimal Binary Search Trees.- 5. Balanced Trees.- 5.1 Weight-Balanced Trees.- 5.2 Height-Balanced Trees.- 5.3 AdvancedTopicson(a,b)-Trees.- 5.3.1 Mergable Priority Queues.- 5.3.2 Amortized Rebalancing Cost and Sorting Presorted Files.- 5.3.3 Finger Trees.- 5.3.4 Fringe Analysis.- 6. Dynamic Weighted Trees.- 6.1 Self-Organizing Data Structures and Their Amortized and Average Case Analysis.- 6.1.1 Self-Organizing Linear Lists.- 6.1.2 Splay Trees.- 6.2 D-trees.- 6.3 An Application to Multidimensional Searching.- 7. A Comparison of Search Structures.- 8. Subsets of a Small Universe.- 8.1 The Boolean Array (Bitvector).- 8.2 The O(log log N) Priority Queue.- 8.3 The Union-Find Problem.- 9. Exercises.- 10. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Table of ContentsVII. Multidimensional Data Structures.- 1. A Black Box Approach to Data Structures.- 1.1 Dynamization.- 1.2 Weighting and Weighted Dynamization.- 1.3 Order Decomposable Problems.- 2. Multi-dimensional Searching Problems.- 2.1 D-dimensional Trees and Polygon Trees.- 2.2 Range Trees and Multidimensional Divide and Conquer.- 2.3 Lower Bounds.- 2.3.1 Partial MatchRetrieval in Minimum Space.- 2.3.2 The Spanning Bound.- 3. Exercises.- 4. Bibliographic Notes.- VIII. Computational Geometry.- 1. Convex Polygons.- 2. Convex Hulls.- 3. Voronoi Diagrams and Searching Planar Subdivisions.- 3.1 Voronoi Diagrams.- 3.2 Searching Planar Subdivisions.- 3.2.1 Removal of Large Independent Sets.- 3.2.2 Path Decompositions.- 3.2.3 Searching Dynamic Planar Subdivisions.- 3.3 Applications.- 4. The Sweep Paradigm.- 4.1 Intersection of Line Segments and Other Intersection Problems in the Plane.- 4.2 Triangulation and its Applications.- 4.3 Space Sweep.- 5. The Realm of Orthogonal Objects.- 5.1 Plane Sweep for Iso-Oriented Objects.- 5.1.1 The Interval Tree and its Applications.- 5.1.2 The Priority Search Tree and its Applications.- 5.1.3 Segment Trees.- 5.1.4 Path Decomposition and Plane Sweep for Non-Iso-Oriented Objects.- 5.2 Divide and Conquer on Iso-Oriented Objects.- 5.2.1 The Line Segment Intersection Problem.- 5.2.2 The Measure and Contour Problems.- 5.3 Intersection Problems in Higher-Dimensional Space.- 6. Geometric Transforms.- 6.1 Duality.- 6.2 Inversion.- 7. Exercises.- 8. Bibliographic Notes.- IX. Algorithmic Paradigms.

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Book SynopsisInformatik ist der Schlüssel, um unsere zunehmend digitalisierte Welt zu verstehen! In diesem Buch lesen Sie nicht nur, wie Navis den günstigsten Weg bestimmen, wie so viele Bilder auf eine kleine Speicherkarte passen oder welche Dinge ein Computer eben nicht ausrechnen kann. Mit Papier und Bleistift und den Bastelvorlagen können Sie die Antworten auf diese und viele weitere Fragen selbst buchstäblich begreifen. Ein Computer ist dafür gar nicht nötig! Genau genommen sind im Buch sogar mehrere Computer aus Pappe enthalten, anhand derer man besser versteht, wie die "echten" Geräte gestaltet sind und wie diese funktionieren. Als Neuerung gibt es ergänzende, aktive Webseiten, die Sie frei (und ohne Werbung) aus dem Internet abrufen können, um mit ihnen zu experimentieren. Das Buch ist für alle da, die schon immer mal hinter die Kulissen der Wissenschaft Informatik schauen wollten: Vom Schüler zum Lehrer, vom Studenten zum Professor, vom interessierten Laien zum IT-Experten, der zwar genau weiß, wie er bestimmte Dinge zu tun hat, aber vielleicht nicht, warum sie so funktionieren oder wie er den Kern seiner tägliche Arbeit seiner Familie verständlich machen kann. Die 5. Auflage enthält zusätzliche Kapitel mit neuem Material sowie die Erweiterung und Überarbeitung der vorhandenen Kapitel. Das bewährte Hands-on-Konzept mit Experimenten und Bastelbögen zum Ausschneiden ist der durchgängige rote Faden.Stimmen zu vorhergehenden Auflagen:„Wer mit einem Informatikstudium liebäugelt, erhält einen Vorgeschmack auf das, was ihn erwartet - alle anderen können das Buch einfach zum Vergnügen lesen.“ c't – Magazin für Computertechnik„Lassen Sie sich also ein auf das ‚Abenteuer Informatik’! Ich bin sicher, dass Sie Spaß daran haben“ LOG IN – Informatische Bildung und Computer in der Schule„Auch wenn es unglaublich klingt: Abenteuer Informatik ist ein Buch über wichtige Prinzipien der modernen informationsverarbeitenden Alltagswelt, das man beim Lesen nicht mehr aus der Hand legen will.“ BIOspektrum„Mit bester Empfehlung!" PM – Praxis der Mathematik„Bits zum Begreifen" Bild der WissenschaftProf. Dr. Jens Gallenbacher liegt am Herzen, die Fachwissenschaft Informatik lebendig und mit einem hohen Allgemeinbildungsgrad zu vermitteln. Er ist an der Johannes Gutenberg-Universität in Mainz für die Ausbildung neuer Informatiklehrerinnen und -lehrer verantwortlich. Um zu zeigen, dass Informatik mehr mit menschlicher Kreativität und konsequentem Denken zu tun hat als mit Computern, verzichtet er dabei weitgehend auf den Einsatz der Geräte. Seine Konzepte werden vom Kindergarten bis zur universitären Grundlagenausbildung eingesetzt.Trade Review“... Erklärungen werden wie in algorithmischen Schritten sehr genau analysiert und führen bis zu wesentlichen Grundlagen der Informatik. ... Weit verständlich, fesselnd, zum Nachdenken und Diskutieren. Mitunter notwendige Geduld wird reich belohnt. Als Vertiefung daneben gut "Algorithmen kapieren" ...” (Rolf Becker-Friedrich, in: ekz-Informationsdienst, Heft 49, 2021)Table of ContentsEinleitung.- 1 Sag' mir wohin ...- 2 Ordnung muss sein!- 3 Ich packe meinen Koffer und ...- 4 Der Trick mit dem Binären.- 5 100000000000 Jahre Informatik?- 6 Von Kamelen und dem Nadelöhr.- 7 Verluste gibt es doch immer!- 8 Erkennungsdienst.- 9 Paketpost.- 10 Alles im Fluss.- 11 Ordnung im Chaos.-12 Mit Sicherheit.- 13 Rechnen mit Strom.- 14 Besser rechnen mit Strom.- 15 Allmächtiger Computer!?.- 16 Spielchen gefällig?- 17 Schnelle Antworten.- 18 Computer auf der Schulbank.- Glossar.

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Book SynopsisThe conical approach provides a geometrical understanding of optimization and is a powerful research tool and useful problem-solving technique (for example, in decision support and real time control applications). Conical optimality conditions are first stated in a very general optimization framework, and then applied to linear programming. A complete theory along with primal and dual algorithms is given, and solutions and algorithms are also provided for vector and robust linear optimization. The advantages of parameter dependence of conical methods are fully discussed. In addition to numerical results, the book provides source codes and detailed documentation of a Modula-2 implementation for the main algorithms.Table of ContentsPart I: General Theory Part II: Further Advanced Results Part III: Implementations and Numerical Results

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Book Synopsis1 Integers.- 2 Congruences and Residue Class Rings.- 3 Encryption.- 4 Probability and Perfect Secrecy.- 5 DES.- 6 AES.- 7 Prime Number Generation.- 8 Public-Key Encryption.- 9 Factoring.- 10 Discrete Logarithms.- 11 Cryptographic Hash Functions.- 12 Digital Signatures.- 13 Other Systems.- 14 Identification.- 15 Secret Sharing.- 16 Public-Key Infrastructures.- Solutions of the exercises.- References.Trade ReviewFrom the reviews: Zentralblatt Math "[......] Of the three books under review, Buchmann's is by far the most sophisticated, complete and up-to-date. It was written for computer-science majors - German ones at that - and might be rough going for all but the best American undergraduates. It is amazing how much Buchmann is able to do in under 300 pages: self-contained explanations of the relevant mathematics (with proofs); a systematic introduction to symmetric cryptosystems, including a detailed description and discussion of DES; a good treatment of primality testing, integer factorization, and algorithms for discrete logarithms, clearly written sections describing most of the major types of cryptosystems, and explanations of basic concepts of practical cryptography such as hash functions, message authentication codes, signatures, passwords, certification authorities, and certificate chains. This book is an excellent reference, and I believe that it would also be a good textbook for a course for mathematics or computer science majors, provided that the instructor is prepared to supplement it with more leisurely treatments of some of the topics." N. Koblitz (Seattle, WA) - American Math. Society Monthly. J.A. Buchmann Introduction to Cryptography "It gives a clear and systematic introduction into the subject whose popularity is ever increasing, and can be recommended to all who would like to learn about cryptography. The book contains many exercises and examples. It can be used as a textbook and is likely to become popular among students. The necessary definitions and concepts from algebra, number theory and probability theory are formulated, illustrated by examples and applied to cryptography." —ZENTRALBLATT MATH "For those of use who wish to learn more about cryptography and/or to teach it, Johannes Buchmann has written this book. … The book is mathematically complete and a satisfying read. There are plenty of homework exercises … . This is a good book for upperclassmen, graduate students, and faculty. … This book makes a superior reference and a fine textbook." (Robert W. Vallin, MathDL, January, 2001) "Buchmann’s book is a text on cryptography intended to be used at the undergraduate level. … the intended audiences of this book are ‘readers who want to learn about modern cryptographic algorithms and their mathematical foundations … . I enjoy reading this book. … Readers will find a good exposition of the techniques used in developing and analyzing these algorithms. … These make Buchmann’s text an excellent choice for self study or as a text for students … in elementary number theory and algebra." (Andrew C. Lee, SIGACT News, Vol. 34 (4), 2003) From the reviews of the second edition: "This is the english translation of the second edition of the author’s prominent german textbook ‘Einführung in die Kryptographie’. The original text grew out of several courses on cryptography given by the author at the Technical University Darmstadt; it is aimed at readers who want to learn about modern cryptographic techniques and its mathematical foundations … . As compared with the first edition the number of exercises has almost been doubled and some material … has been added." (R. Steinbauer, Monatshefte für Mathematik, Vol. 150 (4), 2007)Table of ContentsIntegers.- Congruences and Residue Class Rings.- Encryption.- Probability and Perfect Secrecy.- DES.- AES.- Prime Number Generation.- Public-Key Encryption.- Factoring.- Discrete Logarithms.- Cryptographic Hash Functions.- Digital Signatures.- Other Systems.- Identification.- Public-Key Infrastructures.- Solutions of the Odd Exercises.- Subject Index.- Bibliography.

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Book SynopsisPresents an account of graph theory. Written with students of mathematics and computer science in mind, this book reflects the state of the subject and emphasizes connections with other branches of pure mathematics. It presents a survey of fresh topics and includes more than 600 exercises.Trade Review"...This book is likely to become a classic, and it deserves to be on the shelf of everyone working in graph theory or even remotely related areas, from graduate student to active researcher."--MATHEMATICAL REVIEWSTable of Contents1: Fundamentals. 2: Electrical Networks. 3: Flows, Connectivity and Matching. 4: Extremal Problems. 5: Colouring. 6: Ramsey Theory. 7: Random Graphs. 8: Graphs, Groups and Matrices. 9: Random Walks on Graphs. 10: The Tutte Polynomial.

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Book SynopsisProgrammers, scientists, and engineers are always in need of newer techniques and algorithms to manipulate and interpret images. Algorithms for Image Processing and Computer Vision is an accessible collection of algorithms for common image processing applications that simplifies complicated mathematical calculations.Table of ContentsPreface xxi Chapter 1 Practical Aspects of a Vision System — Image Display, Input/Output, and Library Calls 1 OpenCV 2 The Basic OpenCV Code 2 The IplImage Data Structure 3 Reading and Writing Images 6 Image Display 7 An Example 7 Image Capture 10 Interfacing with the AIPCV Library 14 Website Files 18 References 18 Chapter 2 Edge-Detection Techniques 21 The Purpose of Edge Detection 21 Traditional Approaches and Theory 23 Models of Edges 24 Noise 26 Derivative Operators 30 Template-Based Edge Detection 36 Edge Models: The Marr-Hildreth Edge Detector 39 The Canny Edge Detector 42 The Shen-Castan (ISEF) Edge Detector 48 A Comparison of Two Optimal Edge Detectors 51 Color Edges 53 Source Code for the Marr-Hildreth Edge Detector 58 Source Code for the Canny Edge Detector 62 Source Code for the Shen-Castan Edge Detector 70 Website Files 80 References 82 Chapter 3 Digital Morphology 85 Morphology Defined 85 Connectedness 86 Elements of Digital Morphology — Binary Operations 87 Binary Dilation 88 Implementing Binary Dilation 92 Binary Erosion 94 Implementation of Binary Erosion 100 Opening and Closing 101 MAX — A High-Level Programming Language for Morphology 107 The ‘‘Hit-and-Miss’’ Transform 113 Identifying Region Boundaries 116 Conditional Dilation 116 Counting Regions 119 Grey-Level Morphology 121 Opening and Closing 123 Smoothing 126 Gradient 128 Segmentation of Textures 129 Size Distribution of Objects 130 Color Morphology 131 Website Files 132 References 135 Chapter 4 Grey-Level Segmentation 137 Basics of Grey-Level Segmentation 137 Using Edge Pixels 139 Iterative Selection 140 The Method of Grey-Level Histograms 141 Using Entropy 142 Fuzzy Sets 146 Minimum Error Thresholding 148 Sample Results From Single Threshold Selection 149 The Use of Regional Thresholds 151 Chow and Kaneko 152 Modeling Illumination Using Edges 156 Implementation and Results 159 Comparisons 160 Relaxation Methods 161 Moving Averages 167 Cluster-Based Thresholds 170 Multiple Thresholds 171 Website Files 172 References 173 Chapter 5 Texture and Color 177 Texture and Segmentation 177 A Simple Analysis of Texture in Grey-Level Images 179 Grey-Level Co-Occurrence 182 Maximum Probability 185 Moments 185 Contrast 185 Homogeneity 185 Entropy 186 Results from the GLCM Descriptors 186 Speeding Up the Texture Operators 186 Edges and Texture 188 Energy and Texture 191 Surfaces and Texture 193 Vector Dispersion 193 Surface Curvature 195 Fractal Dimension 198 Color Segmentation 201 Color Textures 205 Website Files 205 References 206 Chapter 6 Thinning 209 What Is a Skeleton? 209 The Medial Axis Transform 210 Iterative Morphological Methods 212 The Use of Contours 221 Choi/Lam/Siu Algorithm 224 Treating the Object as a Polygon 226 Triangulation Methods 227 Force-Based Thinning 228 Definitions 229 Use of a Force Field 230 Subpixel Skeletons 234 Source Code for Zhang-Suen/Stentiford/Holt Combined Algorithm 235 Website Files 246 References 247 Chapter 7 Image Restoration 251 Image Degradations — The Real World 251 The Frequency Domain 253 The Fourier Transform 254 The Fast Fourier Transform 256 The Inverse Fourier Transform 260 Two-Dimensional Fourier Transforms 260 Fourier Transforms in OpenCV 262 Creating Artificial Blur 264 The Inverse Filter 270 The Wiener Filter 271 Structured Noise 273 Motion Blur — A Special Case 276 The Homomorphic Filter — Illumination 277 Frequency Filters in General 278 Isolating Illumination Effects 280 Website Files 281 References 283 Chapter 8 Classification 285 Objects, Patterns, and Statistics 285 Features and Regions 288 Training and Testing 292 Variation: In-Class and Out-Class 295 Minimum Distance Classifiers 299 Distance Metrics 300 Distances Between Features 302 Cross Validation 304 Support Vector Machines 306 Multiple Classifiers — Ensembles 309 Merging Multiple Methods 309 Merging Type 1 Responses 310 Evaluation 311 Converting Between Response Types 312 Merging Type 2 Responses 313 Merging Type 3 Responses 315 Bagging and Boosting 315 Bagging 315 Boosting 316 Website Files 317 References 318 Chapter 9 Symbol Recognition 321 The Problem 321 OCR on Simple Perfect Images 322 OCR on Scanned Images — Segmentation 326 Noise 327 Isolating Individual Glyphs 329 Matching Templates 333 Statistical Recognition 337 OCR on Fax Images — Printed Characters 339 Orientation — Skew Detection 340 The Use of Edges 345 Handprinted Characters 348 Properties of the Character Outline 349 Convex Deficiencies 353 Vector Templates 357 Neural Nets 363 A Simple Neural Net 364 A Backpropagation Net for Digit Recognition 368 The Use of Multiple Classifiers 372 Merging Multiple Methods 372 Results From the Multiple Classifier 375 Printed Music Recognition — A Study 375 Staff Lines 376 Segmentation 378 Music Symbol Recognition 381 Source Code for Neural Net Recognition System 383 Website Files 390 References 392 Chapter 10 Content-Based Search — Finding Images by Example 395 Searching Images 395 Maintaining Collections of Images 396 Features for Query by Example 399 Color Image Features 399 Mean Color 400 Color Quad Tree 400 Hue and Intensity Histograms 401 Comparing Histograms 402 Requantization 403 Results from Simple Color Features 404 Other Color-Based Methods 407 Grey-Level Image Features 408 Grey Histograms 409 Grey Sigma — Moments 409 Edge Density — Boundaries Between Objects 409 Edge Direction 410 Boolean Edge Density 410 Spatial Considerations 411 Overall Regions 411 Rectangular Regions 412 Angular Regions 412 Circular Regions 414 Hybrid Regions 414 Test of Spatial Sampling 414 Additional Considerations 417 Texture 418 Objects, Contours, Boundaries 418 Data Sets 418 Website Files 419 References 420 Systems 424 Chapter 11 High-Performance Computing for Vision and Image Processing 425 Paradigms for Multiple-Processor Computation 426 Shared Memory 426 Message Passing 427 Execution Timing 427 Using clock() 428 Using QueryPerformanceCounter 430 The Message-Passing Interface System 432 Installing MPI 432 Using MPI 433 Inter-Process Communication 434 Running MPI Programs 436 Real Image Computations 437 Using a Computer Network — Cluster Computing 440 A Shared Memory System — Using the PC Graphics Processor 444 GLSL 444 OpenGL Fundamentals 445 Practical Textures in OpenGL 448 Shader Programming Basics 451 Vertex and Fragment Shaders 452 Required GLSL Initializations 453 Reading and Converting the Image 454 Passing Parameters to Shader Programs 456 Putting It All Together 457 Speedup Using the GPU 459 Developing and Testing Shader Code 459 Finding the Needed Software 460 Website Files 461 References 461 Index 465

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Book SynopsisAn object-oriented learning framework for creating good software design. Bruno Preiss presents readers with a modern, object-oriented perspective for looking at data structures and algorithms, clearly showing how to use polymorphism and inheritance, and including fragments from working and tested programs.Table of ContentsAlgorithm Analysis. Asymptotic Notation. Foundational Data Structures. Data Types and Abstraction. Stacks, Queues and Deques. Ordered Lists and Sorted Lists. Hashing, Hash Tables and Scatter Tables. Trees. Search Trees. Heaps and Priority Queues. Sets, Multisets and Partitions. Dynamic Storage Allocation. Algorithmic Patterns and Problem Solvers. Sorting Algorithms and Sorters. Graphs and Graph Algorithms. Appendices. Index.

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Book SynopsisTrade Review"A welcome addition to the growing literature on the digital economy and change." * Choice *"Stimulating." * Paradigm Explorer *

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Book SynopsisTrade Review"Winner of the Bronze Medal in Business Technology, Axiom Business Book Awards""I loved this book. . . . Trading at the Speed of Light is an amazing, detailed account of why material reality matters for virtual outcomes, and conversely, in the financial markets. Everybody with the slightest interest in modern finance should read it."---Diane Coyle, Enlightened Economist

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Book SynopsisAn introduction to learning how to protect ourselves and organise against Big DataTrade Review'A call to arms [...] sets out a clear, persuasive argument for the need to challenge the power of platforms and systems, and details the tools to do so. A thought-provoking read' -- Prof. Rob Kitchin, Maynooth University‘The first non-technical guidebook on how to live with location data and it is a truly radical response for our times. Spatial data for us, not about us’ -- Jeremy W. Crampton, Professor of Urban Data Analysis, Newcastle University‘Brilliantly traces the closed loops of spatial data and suggests new escape routes, reminding us that our data can be remade to tell different stories’ -- Professor Kate Crawford, author of ‘Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence’'The book that I’ve long been waiting for, one that takes a material approach to the data geographies informing and being informed by technologies of everyday life’ -- Erin McElroy, Assistant Professor of American and Digital Studies at the University of Texas at Austin and cofounder of the Anti-Eviction Mapping Project'Data Power is an activist handbook wrapped in a theoretical treatise inside a media manifesto. The authors have a lively set of suggestions that provide a welcome antidote to the temptations of resignation and complacency' -- Mark Andrejevic, Professor in the School of Media, Film, and Journalism at Monash UniversityTable of ContentsList of Figures and Tables Series Preface Acknowledgments List of Abbreviations Introduction: Technology and the Axes of Hope and Fear 1. Life in the Age of Big Data 2. What Are Our Data, and What Are They Worth? 3. Existing Everyday Resistances 4. Contesting the Data Spectacle 5. Our Data Are Us, So Make Them Ours Epilogue Notes Bibliography Index

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Book Synopsis

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Book SynopsisData Mining Algorithms is a practical, technically-oriented guide to data mining algorithms that covers the most important algorithms for building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality evaluation, and creating model ensembles. The author presents many of the important topics and methodologies widely used in data mining, whilst demonstrating the internal operation and usage of data mining algorithms using examples in R.Table of ContentsAcknowledgements xix Preface xxi References xxxi Part I Preliminaries 1 1 Tasks 3 1.1 Introduction 3 1.2 Inductive learning tasks 5 1.3 Classification 9 1.4 Regression 14 1.5 Clustering 16 1.6 Practical issues 19 1.7 Conclusion 20 1.8 Further readings 21 References 22 2 Basic statistics 23 2.1 Introduction 23 2.2 Notational conventions 24 2.3 Basic statistics as modeling 24 2.4 Distribution description 25 2.5 Relationship detection 47 2.6 Visualization 62 2.7 Conclusion 65 2.8 Further readings 66 References 67 Part II Classification 69 3 Decision trees 71 3.1 Introduction 71 3.2 Decision tree model 72 3.3 Growing 76 3.4 Pruning 90 3.5 Prediction 103 3.6 Weighted instances 105 3.7 Missing value handling 106 3.8 Conclusion 114 3.9 Further readings 114 References 116 4 Naïve Bayes classifier 118 4.1 Introduction 118 4.2 Bayes rule 118 4.3 Classification by Bayesian inference 120 4.4 Practical issues 125 4.5 Conclusion 131 4.6 Further readings 131 References 132 5 Linear classification 134 5.1 Introduction 134 5.2 Linear representation 136 5.3 Parameter estimation 145 5.4 Discrete attributes 154 5.5 Conclusion 155 5.6 Further readings 156 References 157 6 Misclassification costs 159 6.1 Introduction 159 6.2 Cost representation 161 6.3 Incorporating misclassification costs 164 6.4 Effects of cost incorporation 176 6.5 Experimental procedure 180 6.6 Conclusion 184 6.7 Further readings 185 References 187 7 Classification model evaluation 189 7.1 Introduction 189 7.2 Performance measures 190 7.3 Evaluation procedures 213 7.4 Conclusion 231 7.5 Further readings 232 References 233 Part III Regression 235 8 Linear regression 237 8.1 Introduction 237 8.2 Linear representation 238 8.3 Parameter estimation 242 8.4 Discrete attributes 250 8.5 Advantages of linear models 251 8.6 Beyond linearity 252 8.7 Conclusion 258 8.8 Further readings 258 References 259 9 Regression trees 261 9.1 Introduction 261 9.2 Regression tree model 262 9.3 Growing 263 9.4 Pruning 274 9.5 Prediction 277 9.6 Weighted instances 278 9.7 Missing value handling 279 9.8 Piecewise linear regression 284 9.9 Conclusion 292 9.10 Further readings 292 References 293 10 Regression model evaluation 295 10.1 Introduction 295 10.2 Performance measures 296 10.3 Evaluation procedures 303 10.4 Conclusion 309 10.5 Further readings 309 References 310 Part IV Clustering 311 11 (Dis)similarity measures 313 11.1 Introduction 313 11.2 Measuring dissimilarity and similarity 313 11.3 Difference-based dissimilarity 314 11.4 Correlation-based similarity 321 11.5 Missing attribute values 324 11.6 Conclusion 325 11.7 Further readings 325 References 326 12 k-Centers clustering 328 12.1 Introduction 328 12.2 Algorithm scheme 330 12.3 k-Means 334 12.4 Beyond means 338 12.5 Beyond (fixed) k 342 12.6 Explicit cluster modeling 343 12.7 Conclusion 345 12.8 Further readings 345 References 347 13 Hierarchical clustering 349 13.1 Introduction 349 13.2 Cluster hierarchies 351 13.3 Agglomerative clustering 353 13.4 Divisive clustering 361 13.5 Hierarchical clustering visualization 364 13.6 Hierarchical clustering prediction 366 13.7 Conclusion 369 13.8 Further readings 370 References 371 14 Clustering model evaluation 373 14.1 Introduction 373 14.2 Per-cluster quality measures 376 14.3 Overall quality measures 385 14.4 External quality measures 393 14.5 Using quality measures 397 14.6 Conclusion 398 14.7 Further readings 398 References 399 Part V Getting Better Models 401 15 Model ensembles 403 15.1 Introduction 403 15.2 Model committees 404 15.3 Base models 406 15.4 Model aggregation 420 15.5 Specific ensemble modeling algorithms 431 15.6 Quality of ensemble predictions 448 15.7 Conclusion 449 15.8 Further readings 450 References 451 16 Kernel methods 454 16.1 Introduction 454 16.2 Support vector machines 457 16.3 Support vector regression 473 16.4 Kernel trick 482 16.5 Kernel functions 484 16.6 Kernel prediction 487 16.7 Kernel-based algorithms 489 16.8 Conclusion 494 16.9 Further readings 495 References 496 17 Attribute transformation 498 17.1 Introduction 498 17.2 Attribute transformation task 499 17.3 Simple transformations 504 17.4 Multiclass encoding 510 17.5 Conclusion 521 17.6 Further readings 521 References 522 18 Discretization 524 18.1 Introduction 524 18.2 Discretization task 525 18.3 Unsupervised discretization 530 18.4 Supervised discretization 533 18.5 Effects of discretization 551 18.6 Conclusion 553 18.7 Further readings 553 References 556 19 Attribute selection 558 19.1 Introduction 558 19.2 Attribute selection task 559 19.3 Attribute subset search 562 19.4 Attribute selection filters 568 19.5 Attribute selection wrappers 588 19.6 Effects of attribute selection 593 19.7 Conclusion 598 19.8 Further readings 599 References 600 20 Case studies 602 20.1 Introduction 602 20.2 Census income 605 20.3 Communities and crime 631 20.4 Cover type 640 20.5 Conclusion 654 20.6 Further readings 655 References 655 Closing 657 A Notation 659 A.1 Attribute values 659 A.2 Data subsets 659 A.3 Probabilities 660 B R packages 661 B.1 CRAN packages 661 B.2 DMR packages 662 B.3 Installing packages 663 References 664 C Datasets 666 Index 667

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Book SynopsisThis comprehensive guide describes how evolutionary algorithms (EA) may be used to identify, model, and optimize day-to-day problems that arise for researchers in optimization and mobile networking.Table of ContentsPreface xiii PART I BASIC CONCEPTS AND LITERATURE REVIEW 1 1 INTRODUCTION TO MOBILE AD HOC NETWORKS 3 1.1 Mobile Ad Hoc Networks 6 1.2 Vehicular Ad Hoc Networks 9 1.2.1 Wireless Access in Vehicular Environment (WAVE) 11 1.2.2 Communication Access for Land Mobiles (CALM) 12 1.2.3 C2C Network 13 1.3 Sensor Networks 14 1.3.1 IEEE 1451 17 1.3.2 IEEE 802.15.4 17 1.3.3 ZigBee 18 1.3.4 6LoWPAN 19 1.3.5 Bluetooth 19 1.3.6 Wireless Industrial Automation System 20 1.4 Conclusion 20 References 21 2 INTRODUCTION TO EVOLUTIONARY ALGORITHMS 27 2.1 Optimization Basics 28 2.2 Evolutionary Algorithms 29 2.3 Basic Components of Evolutionary Algorithms 32 2.3.1 Representation 32 2.3.2 Fitness Function 32 2.3.3 Selection 32 2.3.4 Crossover 33 2.3.5 Mutation 34 2.3.6 Replacement 35 2.3.7 Elitism 35 2.3.8 Stopping Criteria 35 2.4 Panmictic Evolutionary Algorithms 36 2.4.1 Generational EA 36 2.4.2 Steady-State EA 36 2.5 Evolutionary Algorithms with Structured Populations 36 2.5.1 Cellular EAs 37 2.5.2 Cooperative Coevolutionary EAs 38 2.6 Multi-Objective Evolutionary Algorithms 39 2.6.1 Basic Concepts in Multi-Objective Optimization 40 2.6.2 Hierarchical Multi-Objective Problem Optimization 42 2.6.3 Simultaneous Multi-Objective Problem Optimization 43 2.7 Conclusion 44 References 45 3 SURVEY ON OPTIMIZATION PROBLEMS FOR MOBILE AD HOC NETWORKS 49 3.1 Taxonomy of the Optimization Process 51 3.1.1 Online and Offline Techniques 51 3.1.2 Using Global or Local Knowledge 52 3.1.3 Centralized and Decentralized Systems 52 3.2 State of the Art 53 3.2.1 Topology Management 53 3.2.2 Broadcasting Algorithms 58 3.2.3 Routing Protocols 59 3.2.4 Clustering Approaches 63 3.2.5 Protocol Optimization 64 3.2.6 Modeling the Mobility of Nodes 65 3.2.7 Selfish Behaviors 66 3.2.8 Security Issues 67 3.2.9 Other Applications 67 3.3 Conclusion 68 References 69 4 MOBILE NETWORKS SIMULATION 79 4.1 Signal Propagation Modeling 80 4.1.1 Physical Phenomena 81 4.1.2 Signal Propagation Models 85 4.2 State of the Art of Network Simulators 89 4.2.1 Simulators 89 4.2.2 Analysis 92 4.3 Mobility Simulation 93 4.3.1 Mobility Models 93 4.3.2 State of the Art of Mobility Simulators 96 4.4 Conclusion 98 References 98 PART II PROBLEMS OPTIMIZATION 105 5 PROPOSED OPTIMIZATION FRAMEWORK 107 5.1 Architecture 108 5.2 Optimization Algorithms 110 5.2.1 Single-Objective Algorithms 110 5.2.2 Multi-Objective Algorithms 115 5.3 Simulators 121 5.3.1 Network Simulator: ns-3 121 5.3.2 Mobility Simulator: SUMO 123 5.3.3 Graph-Based Simulations 126 5.4 Experimental Setup 127 5.5 Conclusion 131 References 131 6 BROADCASTING PROTOCOL 135 6.1 The Problem 136 6.1.1 DFCN Protocol 136 6.1.2 Optimization Problem Definition 138 6.2 Experiments 140 6.2.1 Algorithm Configurations 140 6.2.2 Comparison of the Performance of the Algorithms 141 6.3 Analysis of Results 142 6.3.1 Building a Representative Subset of Best Solutions 143 6.3.2 Interpretation of the Results 145 6.3.3 Selected Improved DFCN Configurations 148 6.4 Conclusion 150 References 151 7 ENERGY MANAGEMENT 153 7.1 The Problem 154 7.1.1 AEDB Protocol 154 7.1.2 Optimization Problem Definition 156 7.2 Experiments 159 7.2.1 Algorithm Configurations 159 7.2.2 Comparison of the Performance of the Algorithms 160 7.3 Analysis of Results 161 7.4 Selecting Solutions from the Pareto Front 164 7.4.1 Performance of the Selected Solutions 167 7.5 Conclusion 170 References 171 8 NETWORK TOPOLOGY 173 8.1 The Problem 175 8.1.1 Injection Networks 175 8.1.2 Optimization Problem Definition 176 8.2 Heuristics 178 8.2.1 Centralized 178 8.2.2 Distributed 179 8.3 Experiments 180 8.3.1 Algorithm Configurations 180 8.3.2 Comparison of the Performance of the Algorithms 180 8.4 Analysis of Results 183 8.4.1 Analysis of the Objective Values 183 8.4.2 Comparison with Heuristics 185 8.5 Conclusion 187 References 188 9 REALISTIC VEHICULAR MOBILITY 191 9.1 The Problem 192 9.1.1 Vehicular Mobility Model 192 9.1.2 Optimization Problem Definition 196 9.2 Experiments 199 9.2.1 Algorithms Configuration 199 9.2.2 Comparison of the Performance of the Algorithms 200 9.3 Analysis of Results 202 9.3.1 Analysis of the Decision Variables 202 9.3.2 Analysis of the Objective Values 204 9.4 Conclusion 206 References 206 10 SUMMARY AND DISCUSSION 209 10.1 A New Methodology for Optimization in Mobile Ad Hoc Networks 211 10.2 Performance of the Three Algorithmic Proposals 213 10.2.1 Broadcasting Protocol 213 10.2.2 Energy-Efficient Communications 214 10.2.3 Network Connectivity 214 10.2.4 Vehicular Mobility 215 10.3 Global Discussion on the Performance of the Algorithms 215 10.3.1 Single-Objective Case 216 10.3.2 Multi-Objective Case 217 10.4 Conclusion 218 References 218 INDEX 221

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Book SynopsisThe confluence of cloud computing, parallelism and advanced machine intelligence approaches has created a world in which the optimum knowledge system will usually be architected from the combination of two or more knowledge-generating systems. There is a need, then, to provide a reusable, broadly-applicable set of design patterns to empower the intelligent system architect to take advantage of this opportunity. This book explains how to design and build intelligent systems that are optimized for changing system requirements (adaptability), optimized for changing system input (robustness), and optimized for one or more other important system parameters (e.g., accuracy, efficiency, cost). It provides an overview of traditional parallel processing which is shown to consist primarily of task and component parallelism; before introducing meta-algorithmic parallelism which is based on combining two or more algorithms, classification engines or other systems. Key features:Table of Contents1 Introduction and Overview 1 1.1 Introduction 1 1.2 Why Is This Book Important? 2 1.3 Organization of the Book 3 1.4 Informatics 4 1.5 Ensemble Learning 6 1.6 Machine Learning/Intelligence 7 1.7 Artificial Intelligence 22 1.8 Data Mining/Knowledge Discovery 31 1.9 Classification 32 1.10 Recognition 38 1.11 System-Based Analysis 39 1.12 Summary 39 References 40 2 Parallel Forms of Parallelism 42 2.1 Introduction 42 2.2 Parallelism by Task 43 2.3 Parallelism by Component 52 2.4 Parallelism by Meta-algorithm 64 2.5 Summary 71 References 72 3 Domain Areas: Where Is This Relevant? 73 3.1 Introduction 73 3.2 Overview of the Domains 74 3.3 Primary Domains 75 3.4 Secondary Domains 86 3.5 Summary 101 References 102 4 Applications of Parallelism by Task 104 4.1 Introduction 104 4.2 Primary Domains 105 4.3 Summary 135 References 136 5 Application of Parallelism by Component 137 5.1 Introduction 137 5.2 Primary Domains 138 5.3 Summary 172 References 173 6 Introduction to Meta-algorithmics 175 6.1 Introduction 175 6.2 First-Order Meta-algorithmics 178 6.3 Second-Order Meta-algorithmics 195 6.4 Third-Order Meta-algorithmics 218 6.5 Summary 240 References 240 7 First-Order Meta-algorithmics and Their Applications 241 7.1 Introduction 241 7.2 First-Order Meta-algorithmics and the “Black Box” 241 7.3 Primary Domains 242 7.4 Secondary Domains 257 7.5 Summary 271 References 271 8 Second-Order Meta-algorithmics and Their Applications 272 8.1 Introduction 272 8.2 Second-Order Meta-algorithmics and Targeting the “Fringes” 273 8.3 Primary Domains 279 8.4 Secondary Domains 304 8.5 Summary 308 References 308 9 Third-Order Meta-algorithmics and Their Applications 310 9.1 Introduction 310 9.2 Third-Order Meta-algorithmic Patterns 311 9.3 Primary Domains 313 9.4 Secondary Domains 328 9.5 Summary 340 References 341 10 Building More Robust Systems 342 10.1 Introduction 342 10.2 Summarization 342 10.3 Cloud Systems 350 10.4 Mobile Systems 353 10.5 Scheduling 355 10.6 Classification 356 10.7 Summary 358 Reference 359 11 The Future 360 11.1 Recapitulation 360 11.2 The Pattern of all Patience 362 11.3 Beyond the Pale 365 11.4 Coming Soon 367 11.5 Summary 368 References 368 Index

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Book SynopsisEfficient Single Board Computers (SBCs) and advanced VLSI systems have resulted in edge analytics and faster decision making. The QoS parameters like energy, delay, reliability, security, and throughput should be improved on seeking better intelligent expert systems. The resource constraints in the Edge devices, challenges the researchers to meet the required QoS. Since these devices and components work in a remote unattended environment, an optimum methodology to improve its lifetime has become mandatory. Continuous monitoring of events is mandatory to avoid tragic situations; it can only be enabled by providing high QoS. The applications of IoT in digital twin development, health care, traffic analysis, home surveillance, intelligent agriculture monitoring, defense and all common day to day activities have resulted in pioneering embedded devices, which can offer high computational facility without much latency and delay. The book address industrial problems in designing expert systemTable of ContentsPreface xv 1 Smart Health Care Development: Challenges and Solutions 1R. Sujatha, E.P. Ephzibah and S. Sree Dharinya 1.1 Introduction 2 1.2 ICT Explosion 3 1.2.1 RFID 4 1.2.2 IoT and Big Data 5 1.2.3 Wearable Sensors—Head to Toe 7 1.2.4 Cloud Computing 8 1.3 Intelligent Healthcare 10 1.4 Home Healthcare 11 1.5 Data Analytics 11 1.6 Technologies—Data Cognitive 13 1.6.1 Machine Learning 13 1.6.2 Image Processing 14 1.6.3 Deep Learning 14 1.7 Adoption Technologies 15 1.8 Conclusion 15 References 15 2 Working of Mobile Intelligent Agents on the Web—A Survey 21P.R. Joe Dhanith and B. Surendiran 2.1 Introduction 21 2.2 Mobile Crawler 23 2.3 Comparative Study of the Mobile Crawlers 47 2.4 Conclusion 47 References 47 3 Power Management Scheme for Photovoltaic/Battery Hybrid System in Smart Grid 49T. Bharani Prakash and S. Nagakumararaj 3.1 Power Management Scheme 50 3.2 Internal Power Flow Management 50 3.2.1 PI Controller 51 3.2.2 State of Charge 53 3.3 Voltage Source Control 54 3.3.1 Phase-Locked Loop 55 3.3.2 Space Vector Pulse Width Modulation 56 3.3.3 Park Transformation (abc to dq0) 57 3.4 Simulation Diagram and Results 58 3.4.1 Simulation Diagram 58 3.4.2 Simulation Results 63 Conclusion 65 4 Analysis: A Neural Network Equalizer for Channel Equalization by Particle Swarm Optimization for Various Channel Models 67M. Muthumari, D.C. Diana and C. Ambika Bhuvaneswari 4.1 Introduction 68 4.2 Channel Equalization 72 4.2.1 Channel Models 73 4.2.1.1 Tapped Delay Line Model 74 4.2.1.2 Stanford University Interim (SUI) Channel Models 75 4.2.2 Artificial Neural Network 75 4.3 Functional Link Artificial Neural Network 76 4.4 Particle Swarm Optimization 76 4.5 Result and Discussion 77 4.5.1 Convergence Analysis 77 4.5.2 Comparison Between Different Parameters 79 4.5.3 Comparison Between Different Channel Models 80 4.6 Conclusion 81 References 82 5 Implementing Hadoop Container Migrations in OpenNebula Private Cloud Environment 85P. Kalyanaraman, K.R. Jothi, P. Balakrishnan, R.G. Navya, A. Shah and V. Pandey 5.1 Introduction 86 5.1.1 Hadoop Architecture 86 5.1.2 Hadoop and Big Data 88 5.1.3 Hadoop and Virtualization 88 5.1.4 What is OpenNebula? 89 5.2 Literature Survey 90 5.2.1 Performance Analysis of Hadoop 90 5.2.2 Evaluating Map Reduce on Virtual Machines 91 5.2.3 Virtualizing Hadoop Containers 94 5.2.4 Optimization of Hadoop Cluster Using Cloud Platform 95 5.2.5 Heterogeneous Clusters in Cloud Computing 96 5.2.6 Performance Analysis and Optimization in Hadoop 97 5.2.7 Virtual Technologies 97 5.2.8 Scheduling 98 5.2.9 Scheduling of Hadoop VMs 98 5.3 Discussion 99 5.4 Conclusion 100 References 101 6 Transmission Line Inspection Using Unmanned Aerial Vehicle 105A. Mahaboob Subahani, M. Kathiresh and S. Sanjeev 6.1 Introduction 106 6.1.1 Unmanned Aerial Vehicle 106 6.1.2 Quadcopter 106 6.2 Literature Survey 107 6.3 System Architecture 108 6.4 ArduPilot 109 6.5 Arduino Mega 111 6.6 Brushless DC Motor 111 6.7 Battery 112 6.8 CMOS Camera 113 6.9 Electronic Speed Control 113 6.10 Power Module 115 6.11 Display Shield 116 6.12 Navigational LEDS 116 6.13 Role of Sensors in the Proposed System 118 6.13.1 Accelerometer and Gyroscope 118 6.13.2 Magnetometer 118 6.13.3 Barometric Pressure Sensor 119 6.13.4 Global Positioning System 119 6.14 Wireless Communication 120 6.15 Radio Controller 120 6.16 Telemetry Radio 121 6.17 Camera Transmitter 121 6.18 Results and Discussion 121 6.19 Conclusion 124 References 125 7 Smart City Infrastructure Management System Using IoT 127S. Ramamoorthy, M. Kowsigan, P. Balasubramanie and P. John Paul 7.1 Introduction 128 7.2 Major Challenges in IoT-Based Technology 129 7.2.1 Peer to Peer Communication Security 129 7.2.2 Objective of Smart Infrastructure 130 7.3 Internet of Things (IoT) 131 7.3.1 Key Components of Components of IoT 131 7.3.1.1 Network Gateway 132 7.3.1.2 HTTP (HyperText Transfer Protocol) 132 7.3.1.3 LoRaWan (Long Range Wide Area Network) 133 7.3.1.4 Bluetooth 133 7.3.1.5 ZigBee 133 7.3.2 IoT Data Protocols 133 7.3.2.1 Message Queue Telemetry Transport (MQTT) 133 7.3.2.2 Constrained Application Protocol (CoAP) 134 7.3.2.3 Advanced Message Queuing Protocol (AMQP) 134 7.3.2.4 Data Analytics 134 7.4 Machine Learning-Based Smart Decision-Making Process 135 7.5 Cloud Computing 136 References 138 8 Lightweight Cryptography Algorithms for IoT Resource-Starving Devices 139S. Aruna, G. Usha, P. Madhavan and M.V. Ranjith Kumar 8.1 Introduction 139 8.1.1 Need of the Cryptography 140 8.2 Challenges on Lightweight Cryptography 141 8.3 Hashing Techniques on Lightweight Cryptography 142 8.4 Applications on Lighweight Cryptography 152 8.5 Conclusion 167 References 168 9 Pre-Learning-Based Semantic Segmentation for LiDAR Point Cloud Data Using Self-Organized Map 171K. Rajathi and P. Sarasu 9.1 Introduction 172 9.2 Related Work 173 9.2.1 Semantic Segmentation for Images 173 9.3 Semantic Segmentation for LiDAR Point Cloud 173 9.4 Proposed Work 175 9.4.1 Data Acquisition 175 9.4.2 Our Approach 175 9.4.3 Pre-Learning Processing 179 9.5 Region of Interest (RoI) 180 9.6 Registration of Point Cloud 181 9.7 Semantic Segmentation 181 9.8 Self-Organized Map (SOM) 182 9.9 Experimental Result 183 9.10 Conclusion 186 References 187 10 Smart Load Balancing Algorithms in Cloud Computing—A Review 189K.R. Jothi, S. Anto, M. Kohar, M. Chadha and P. Madhavan 10.1 Introduction 189 10.2 Research Challenges 192 10.2.1 Security & Routing 192 10.2.2 Storage/Replication 192 10.2.3 Spatial Spread of the Cloud Nodes 192 10.2.4 Fault Tolerance 193 10.2.5 Algorithm Complexity 193 10.3 Literature Survey 193 10.4 Survey Table 201 10.5 Discussion & Comparison 202 10.6 Conclusion 202 References 216 11 A Low-Cost Wearable Remote Healthcare Monitoring System 219Konguvel Elango and Kannan Muniandi 11.1 Introduction 219 11.1.1 Problem Statement 220 11.1.2 Objective of the Study 221 11.2 Related Works 222 11.2.1 Remote Healthcare Monitoring Systems 222 11.2.2 Pulse Rate Detection 224 11.2.3 Temperate Measurement 225 11.2.4 Fall Detection 225 11.3 Methodology 226 11.3.1 NodeMCU 226 11.3.2 Pulse Rate Detection System 227 11.3.3 Fall Detection System 230 11.3.4 Temperature Detection System 231 11.3.5 LCD Specification 234 11.3.6 ADC Specification 234 11.4 Results and Discussions 236 11.4.1 System Implementation 236 11.4.2 Fall Detection Results 236 11.4.3 ThingSpeak 236 11.5 Conclusion 239 11.6 Future Scope 240 References 241 12 IoT-Based Secure Smart Infrastructure Data Management 243R. Poorvadevi, M. Kowsigan, P. Balasubramanie and J. Rajeshkumar 12.1 Introduction 244 12.1.1 List of Security Threats Related to the Smart IoT Network 244 12.1.2 Major Application Areas of IoT 244 12.1.3 IoT Threats and Security Issues 245 12.1.4 Unpatched Vulnerabilities 245 12.1.5 Weak Authentication 245 12.1.6 Vulnerable API’s 245 12.2 Types of Threats to Users 245 12.3 Internet of Things Security Management 246 12.3.1 Managing IoT Devices 246 12.3.2 Role of External Devices in IoT Platform 247 12.3.3 Threats to Other Computer Networks 248 12.4 Significance of IoT Security 249 12.4.1 Aspects of Workplace Security 249 12.4.2 Important IoT Security Breaches and IoT Attacks 250 12.5 IoT Security Tools and Legislation 250 12.6 Protection of IoT Systems and Devices 251 12.6.1 IoT Issues and Security Challenges 251 12.6.2 Providing Secured Connections 252 12.7 Five Ways to Secure IoT Devices 253 12.8 Conclusion 255 References 255 13 A Study of Addiction Behavior for Smart Psychological Health Care System 257V. Sabapathi and K.P. Vijayakumar 13.1 Introduction 258 13.2 Basic Criteria of Addiction 258 13.3 Influencing Factors of Addiction Behavior 259 13.3.1 Peers Influence 259 13.3.2 Environment Influence 260 13.3.3 Media Influence 262 13.3.4 Family Group and Society 262 13.4 Types of Addiction and Their Effects 262 13.4.1 Gaming Addiction 263 13.4.2 Pornography Addiction 264 13.4.3 Smart Phone Addiction 265 13.4.4 Gambling Addiction 267 13.4.5 Food Addiction 267 13.4.6 Sexual Addiction 268 13.4.7 Cigarette and Alcohol Addiction 268 13.4.8 Status Expressive Addiction 269 13.4.9 Workaholic Addiction 269 13.5 Conclusion 269 References 270 14 A Custom Cluster Design With Raspberry Pi for Parallel Programming and Deployment of Private Cloud 273Sukesh, B., Venkatesh, K. and Srinivas, L.N.B. 14.1 Introduction 274 14.2 Cluster Design with Raspberry Pi 276 14.2.1 Assembling Materials for Implementing Cluster 276 14.2.1.1 Raspberry Pi4 277 14.2.1.2 RPi 4 Model B Specifications 277 14.2.2 Setting Up Cluster 278 14.2.2.1 Installing Raspbian and Configuring Master Node 279 14.2.2.2 Installing MPICH and MPI4PY 279 14.2.2.3 Cloning the Slave Nodes 279 14.3 Parallel Computing and MPI on Raspberry Pi Cluster 279 14.4 Deployment of Private Cloud on Raspberry Pi Cluster 281 14.4.1 NextCloud Software 281 14.5 Implementation 281 14.5.1 NextCloud on RPi Cluster 281 14.5.2 Parallel Computing on RPi Cluster 282 14.6 Results and Discussions 286 14.7 Conclusion 287 References 287 15 Energy Efficient Load Balancing Technique for Distributed Data Transmission Using Edge Computing 289Karthikeyan, K. and Madhavan, P. 15.1 Introduction 290 15.2 Energy Efficiency Offloading Data Transmission 290 15.2.1 Web-Based Offloading 291 15.3 Energy Harvesting 291 15.3.1 LODCO Algorithm 292 15.4 User-Level Online Offloading Framework (ULOOF) 293 15.5 Frequency Scaling 294 15.6 Computation Offloading and Resource Allocation 295 15.7 Communication Technology 296 15.8 Ultra-Dense Network 297 15.9 Conclusion 299 References 299 16 Blockchain-Based SDR Signature Scheme With Time-Stamp 303Swathi Singh, Divya Satish and Sree Rathna Lakshmi 16.1 Introduction 303 16.2 Literature Study 304 16.2.1 Signatures With Hashes 304 16.2.2 Signature Scheme With Server Support 305 16.2.3 Signatures Scheme Based on Interaction 305 16.3 Methodology 306 16.3.1 Preliminaries 306 16.3.1.1 Hash Trees 306 16.3.1.2 Chains of Hashes 306 16.3.2 Interactive Hash-Based Signature Scheme 307 16.3.3 Significant Properties of Hash-Based Signature Scheme 309 16.3.4 Proposed SDR Scheme Structure 310 16.3.4.1 One-Time Keys 310 16.3.4.2 Server Behavior Authentication 310 16.3.4.3 Pre-Authentication by Repository 311 16.4 SDR Signature Scheme 311 16.4.1 Pre-Requisites 311 16.4.2 Key Generation Algorithm 312 16.4.2.1 Server 313 16.4.3 Sign Algorithm 313 16.4.3.1 Signer 313 16.4.3.2 Server 313 16.4.3.3 Repository 314 16.4.4 Verification Algorithm 314 16.5 Supportive Theory 315 16.5.1 Signing Algorithm Supported by Server 315 16.5.2 Repository Deployment 316 16.5.3 SDR Signature Scheme Setup 316 16.5.4 Results and Observation 316 16.6 Conclusion 317 References 317 Index 321

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Book SynopsisALGORITHMS IN BIOINFORMATICS Explore a comprehensive and insightful treatment of the practical application of bioinformatic algorithms in a variety of fields Algorithms in Bioinformatics: Theory and Implementation delivers a fulsome treatment of some of the main algorithms used to explain biological functions and relationships. It introduces readers to the art of algorithms in a practical manner which is linked with biological theory and interpretation. The book covers many key areas of bioinformatics, including global and local sequence alignment, forced alignment, detection of motifs, Sequence logos, Markov chains or information entropy. Other novel approaches are also described, such as Self-Sequence alignment, Objective Digital Stains (ODSs) or Spectral Forecast and the Discrete Probability Detector (DPD) algorithm. The text incorporates graphical illustrations to highlight and emphasize the technical details oTable of ContentsPreface xv About the Companion Website xvii 1 The Tree of Life (I) 1 1.1 Introduction 1 1.2 Emergence of Life 1 1.2.1 Timeline Disagreements 3 1.3 Classifications and Mechanisms 4 1.4 Chromatin Structure 5 1.5 Molecular Mechanisms 9 1.5.1 Precursor Messenger RNA 9 1.5.2 Precursor Messenger RNA to Messenger RNA 10 1.5.3 Classes of Introns 10 1.5.4 Messenger RNA 10 1.5.5 mRNA to Proteins 11 1.5.6 Transfer RNA 12 1.5.7 Small RNA 12 1.5.8 The Transcriptome 13 1.5.9 Gene Networks and Information Processing 13 1.5.10 Eukaryotic vs. Prokaryotic Regulation 14 1.5.11 What Is Life? 14 1.6 Known Species 14 1.7 Approaches for Compartmentalization 15 1.7.1 Two Main Approaches for Organism Formation 16 1.7.2 Size and Metabolism 16 1.8 Sizes in Eukaryotes 16 1.8.1 Sizes in Unicellular Eukaryotes 17 1.8.2 Sizes in Multicellular Eukaryotes 17 1.9 Sizes in Prokaryotes 17 1.10 Virus Sizes 18 1.10.1 Viruses vs. the Spark of Metabolism 20 1.11 The Diffusion Coefficient 20 1.12 The Origins of Eukaryotic Cells 21 1.12.1 Endosymbiosis Theory 21 1.12.2 DNA and Organelles 22 1.12.3 Membrane-bound Organelles with DNA 23 1.12.4 Membrane-bound Organelles Without DNA 23 1.12.5 Control and Division of Organelles 24 1.12.6 The Horizontal Gene Transfer 24 1.12.7 On the Mechanisms of Horizontal Gene Transfer 25 1.13 Origins of Eukaryotic Multicellularity 26 1.13.1 Colonies Inside an Early Unicellular Common Ancestor 26 1.13.2 Colonies of Early Unicellular Common Ancestors 26 1.13.3 Colonies of Inseparable Early Unicellular Common Ancestors 1.13.4 Chimerism and Mosaicism 28 1.14 Conclusions 29 2 Tree of Life: Genomes (II) 31 2.1 Introduction 31 2.2 Rules of Engagement 31 2.3 Genome Sizes in the Tree of Life 32 2.3.1 Alternative Methods 33 2.3.2 The Weaving of Scales 33 2.3.3 Computations on the Average Genome Size 36 2.3.4 Observations on Data 38 2.4 Organellar Genomes 40 2.4.1 Chloroplasts 40 2.4.2 Apicoplasts 40 2.4.3 Chromatophores 42 2.4.4 Cyanelles 42 2.4.5 Kinetoplasts 42 2.4.6 Mitochondria 43 2.5 Plasmids 43 2.6 Virus Genomes 44 2.7 Viroids and Their Implications 46 2.8 Genes vs. Proteins in the Tree of Life 47 2.9 Conclusions 49 3 Sequence Alignment (I) 51 3.1 Introduction 51 3.2 Style and Visualization 51 3.3 Initialization of the Score Matrix 54 3.4 Calculation of Scores 57 3.4.1 Initialization of the Score Matrix for Global Alignment 57 3.4.2 Initialization of the Score Matrix for Local Alignment 62 3.4.3 Optimization of the Initialization Steps 65 3.4.4 Curiosities 66 3.5 Traceback 71 3.6 Global Alignment 75 3.7 Local Alignment 79 3.8 Alignment Layout 84 3.9 Local Sequence Alignment – The Final Version 87 3.10 Complementarity 91 3.11 Conclusions 97 4 Forced Alignment (II) 99 4.1 Introduction 99 4.2 Global and Local Sequence Alignment 100 4.2.1 Short Notes 100 4.2.2 Understanding the Technology 101 4.2.3 Main Objectives 102 4.3 Experiments and Discussions 102 4.3.1 Alignment Layout 106 4.3.2 Forced Alignment Regime 106 4.3.3 Alignment Scores and Significance 109 4.3.4 Optimal Alignments 110 4.3.5 The Main Significance Scores 110 4.3.6 The Information Content 110 4.3.7 The Match Percentage 112 4.3.8 Significance vs. Chance 113 4.3.9 The Importance of Randomness 113 4.3.10 Sequence Quality and the Score Matrix 114 4.3.11 The Significance Threshold 115 4.3.12 Optimal Alignments by Numbers 116 4.3.13 Chaos Theory on Sequence Alignment 116 4.3.14 Image-Encoding Possibilities 116 4.4 Advanced Features and Methods 117 4.4.1 Sequence Detector 117 4.4.2 Parameters 117 4.4.3 Heatmap 118 4.4.4 Text Visualization 123 4.4.5 Graphics for Manuscript Figures and Didactic Presentations 124 4.4.6 Dynamics 124 4.4.7 Independence 125 4.4.8 Limits 125 4.4.9 Local Storage 125 4.5 Conclusions 128 5 Self-Sequence Alignment (I) 129 5.1 Introduction 129 5.2 True Randomness 130 5.3 Information and Compression Algorithms 130 5.4 White Noise and Biological Sequences 131 5.5 The Mathematical Model 131 5.5.1 A Concrete Example 132 5.5.2 Model Dissection 133 5.5.3 Conditions for Maxima and Minima 136 5.6 Noise vs. Redundancy 137 5.7 Global and Local Information Content 137 5.8 Signal Sensitivity 138 5.9 Implementation 140 5.9.1 Global Self-Sequence Alignment 140 5.9.2 Local Self-Sequence Alignment 144 5.10 A Complete Scanner for Information Content 147 5.11 Conclusions 149 6 Frequencies and Percentages (II) 151 6.1 Introduction 151 6.2 Base Composition 152 6.3 Percentage of Nucleotide Combinations 152 6.4 Implementation 153 6.5 A Frequency Scanner 156 6.6 Examples of Known Significance 158 6.7 Observation vs. Expectation 160 6.8 A Frequency Scanner with a Threshold 161 6.9 Conclusions 163 7 Objective Digital Stains (III) 165 7.1 Introduction 165 7.2 Information and Frequency 166 7.3 The Objective Digital Stain 169 7.3.1 A 3D Representation Over a 2D Plane 173 7.3.2 ODSs Relative to the Background 177 7.4 Interpretation of ODSs 181 7.5 The Significance of the Areas in the ODS 183 7.6 Discussions 184 7.6.1 A Similarity Between Dissimilar Sequences 186 7.7 Conclusions 186 8 Detection of Motifs (I) 187 8.1 Introduction 187 8.2 DNA Motifs 187 8.2.1 DNA-binding Proteins vs. Motifs and Degeneracy 188 8.2.2 Concrete Examples of DNA Motifs 188 8.3 Major Functions of DNA Motifs 191 8.3.1 RNA Splicing and DNA Motifs 191 8.4 Conclusions 195 9 Representation of Motifs (II) 197 9.1 Introduction 197 9.2 The Training Data 197 9.3 A Visualization Function 198 9.4 The Alignment Matrix 200 9.5 Alphabet Detection 203 9.6 The Position-Specific Scoring Matrix (PSSM) Initialization 206 9.7 The Position Frequency Matrix (PFM) 207 9.8 The Position Probability Matrix (PPM) 208 9.8.1 A Kind of PPM Pseudo-Scanner 209 9.9 The Position Weight Matrix (PWM) 212 9.10 The Background Model 215 9.11 The Consensus Sequence 218 9.11.1 The Consensus – Not Necessarily Functional 219 9.12 Mutational Intolerance 221 9.13 From Motifs to PWMs 222 9.14 Pseudo-Counts and Negative Infinity 226 9.15 Conclusions 229 10 The Motif Scanner (III) 231 10.1 Introduction 231 10.2 Looking for Signals 232 10.3 A Functional Scanner 235 10.4 The Meaning of Scores 239 10.4.1 A Score Value Above Zero 239 10.4.2 A Score Value Below Zero 241 10.4.3 A Score Value of Zero 241 10.5 Conclusions 242 11 Understanding the Parameters (IV) 243 11.1 Introduction 243 11.2 Experimentation 243 11.2.1 A Scanner Implementation Based on Pseudo-Counts 244 11.2.2 A Scanner Implementation Based on Propagation of Zero Counts 246 11.3 Signal Discrimination 249 11.4 False-Positive Results 250 11.5 Sensitivity Adjustments 251 11.6 Beyond Bioinformatics 252 11.7 A Scanner That Uses a Known PWM 253 11.8 Signal Thresholds 256 11.8.1 Implementation and Filter Testing 258 11.9 Conclusions 262 12 Dynamic Backgrounds (V) 263 12.1 Introduction 263 12.2 Toward a Scanner with Two PFMs 263 12.2.1 The Implementation of Dynamic PWMs 264 12.2.2 Issues and Corrections for Dynamic PWMs 271 12.2.3 Solutions for Aberrant Positive Likelihood Values 274 12.3 A Scanner with Two PFMs 280 12.4 Information and Background Frequencies on Score Values 283 12.5 Dynamic Background vs. Null Model 285 12.6 Conclusions 285 13 Markov Chains: The Machine (I) 287 13.1 Introduction 287 13.2 Transition Matrices 287 13.3 Discrete Probability Detector 292 13.3.1 Alphabet Detection 292 13.3.2 Matrix Initialization 293 13.3.3 Frequency Detection 295 13.3.4 Calculation of Transition Probabilities 297 13.3.5 Particularities in Calculating the Transition Probabilities 306 13.4 Markov Chains Generators 307 13.4.1 The Experiment 308 13.4.2 The Implementation 312 13.4.3 Simulation of Transition Probabilities 315 13.4.4 The Markov machine 315 13.4.5 Result Verification 317 13.5 Conclusions 318 14 Markov Chains: Log Likelihood (II) 319 14.1 Introduction 319 14.2 The Log-Likelihood Matrix 319 14.2.1 A Log-Likelihood Matrix Based on the Null Model 320 14.2.2 A Log-Likelihood Matrix Based on Two Models 322 14.3 Interpretation and Use of the Log-Likelihood Matrix 326 14.4 Construction of a Markov Scanner 328 14.5 A Scanner That Uses a Known LLM 337 14.6 The Meaning of Scores 340 14.7 Beyond Bioinformatics 344 14.8 Conclusions 345 15 Spectral Forecast (I) 347 15.1 Introduction 347 15.2 The Spectral Forecast Model 347 15.3 The Spectral Forecast Equation 349 15.4 The Spectral Forecast Inner Workings 350 15.4.1 Each Part on a Single Matrix 351 15.4.2 Both Parts on a Single Matrix 352 15.4.3 Both Parts on Separate Matrices 353 15.4.4 Concrete Example 1 354 15.4.5 Concrete Example 2 357 15.4.6 Concrete Example 3 359 15.5 Implementations 360 15.5.1 Spectral Forecast for Signals 362 15.5.2 What Does the Value of d Mean? 364 15.5.3 Spectral Forecast for Matrices 368 15.6 The Spectral Forecast Model for Predictions 372 15.6.1 The Spectral Forecast Model for Signals 372 15.6.2 Experiments on the Similarity Index Values 381 15.6.3 The Spectral Forecast Model for Matrices 384 15.7 Conclusions 389 16 Entropy vs. Content (I) 391 16.1 Introduction 391 16.2 Information Entropy 391 16.3 Implementation 395 16.4 Information Content vs. Information Entropy 400 16.4.1 Implementation 403 16.4.2 Additional Considerations 409 16.5 Conclusions 409 17 Philosophical Transactions 411 17.1 Introduction 411 17.2 The Frame of Reference 411 17.2.1 The Fundamental Layer of Complexity 412 17.2.2 On the Complexity of Life 414 17.3 Random vs. Pseudo-random 415 17.4 Random Numbers and Noise 418 17.5 Determinism and Chaos 419 17.5.1 Chaos Without Noise 420 17.5.2 Chaos with Noise 427 17.5.3 Limits of Prediction 430 17.5.4 On the Wings of Chaos 431 17.6 Free Will and Determinism 431 17.6.1 The Greatest Disappointment 432 17.6.2 The Most Powerful Processor in Existence 433 17.6.3 Certainty vs. Interpretation 435 17.6.4 A Wisdom that Applies 436 17.7 Conclusions 439 Appendix A 441 A.1 Association of Numerical Values with Letters 441 A.2 Sorting Values on Columns 443 A.3 The Implementation of a Sequence Logo 446 A.4 Sequence Logos Based on Maximum Values 451 A.5 Using Logarithms to Build Sequence Logos 455 A.6 From a Motif Set to a Sequence Logo 459 References 467 Index 489

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Book SynopsisJan-Willem Middelburg is a Dutch entrepreneur and author with a passion for technology and innovation. He is the CEO and co-founder of Cybiant, a global technology that company that helps to create a more sustainable world through analytics, big data and automation. He is also President and Chief Examiner of the Enterprise Big Data Framework, an independent organization dedicated to upskilling individuals with expertise in Big Data. In partnership with APMG-International, the Enterprise Big Data Framework offers vendor-neutral certifications for individuals.Trade Review"The Enterprise Big Data Framework is relevant for everybody within an organisation engaged in driving maximum benefits from data. There is something for everybody; from the board considering governance and ethical behaviour to individuals within the organisation knowing where they fit and the value they can get from better use of their organisation's data. If you are considering a transformation project, this is an excellent guide for your project team." * Richard Pharro, CEO, The APM Group Limited *"If you are looking for a good guide to empower your knowledge on big data and to find a framework to help you on your big data journey, then this book is for you. From learning what big data is to defining a big data strategy, Jan-Willem has built a book to empower the learner on the topic of big data." * Jordan Morrow, Chief Strategy & Transformation Officer, DataPrime and Author of Be Data Literate *"This book is a master piece for those who are familiar and those who discover the world of data. It provides an "a la carte framework" starting with a (big) data strategy and the supporting aspects such as big data functions, architecture and algorithms. It covers in depth data platforms architectures, its management as well as data governance, data catalogue and all the required security considerations associated to the various data classifications. You will find details of data life cycle management, of various machine learning algorithms and an important chapter covering AI ethics when building and deploying sophisticated algorithms using data. The concepts covered in this book apply to on-premises and in the (public) cloud environments, making this book a must read." * Jean-Michel Coeur, APAC Technology Practice Lead, Data Analytics, Google Cloud *Table of Contents Section - ONE: Introduction to Big Data; Chapter - 01: Introduction to Big Data; Chapter - 02: The Big Data framework; Chapter - 03: Big Data strategy; Chapter - 04: Big Data architecture; Chapter - 05: Big Data algorithms; Chapter - 06: Big Data processes; Chapter - 07: Big Data functions; Chapter - 08: Artificial intelligence; Section - TWO: Enterprise Big Data analysis; Chapter - 09: Introduction to Big Data analysis; Chapter - 10: Defining the business objective; Chapter - 11: Data ingestion – importing and reading data sets; Chapter - 12: Data preparation – cleaning and wrangling data; Chapter - 13: Data analysis – model building; Chapter - 14: Data presentation; Section - THREE: Enterprise Big Data engineering; Chapter - 15: Introduction to Big Data engineering; Chapter - 16: Data modelling; Chapter - 17: Constructing the data lake; Chapter - 18: Building an enterprise Big Data warehouse; Chapter - 19: Design and structure of Big Data pipelines; Chapter - 20: Managing data pipelines; Chapter - 21: Cluster technology; Section - FOUR: enterprise Big Data algorithm design; Chapter - 22: Introduction to Big Data algorithm design; Chapter - 23: Algorithm design – fundamental concepts; Chapter - 24: Statistical machine learning algorithms; Chapter - 25: The data science roadmap; Chapter - 26: Programming languages 26 visualization and simple metrics; Chapter - 27: Advanced machine learning algorithms; Chapter - 28: Advanced machine learning classification algorithms; Chapter - 29: Technical communication and documentation; Section - FIVE: Enterprise Big Data architecture; Chapter - 30: Introduction to the Big Data architecture; Chapter - 31: Strength and resilience – the Big Data platform; Chapter - 32: Design principles for Big Data architecture; Chapter - 33: Big Data infrastructure; Chapter - 34: Big Data platforms; Chapter - 35: The Big Data application provider; Chapter - 36: System orchestration in Big Data

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Book SynopsisUncovers the surprising ways math shapes our livesfrom whom we date to what we learn. How do dating sites match compatible partners? What do cell phones and sea coasts have in common? And why do computer scientists keep ant colonies? Jeff Suzuki answers these questions and more in Patently Mathematical, which explores the mathematics behind some of the key inventions that have changed our world. In recent years, patents based on mathematics have been issued by the thousandsfrom search engines and image recognition technology to educational software and LEGO designs. Suzuki delves into the details of cutting-edge devices, programs, and products to show how even the simplest mathematical principles can be turned into patentable ideas worth billions of dollars. Readers will discover whether secure credit cards are really secure how improved data compression made streaming video services like Netflix a hit the mathematics behind self-correcting golf balls why Google is such an effectiTrade ReviewPatently Mathematical by Jeff Suzuki is a chronicle of the various patents based on mathematical algorithm applications. Each of his twelve chapters itemizes a specific family of patents along with pertinent anecdotes and suitable-for-the-general-reader examples illustrating how the algorithms work . . . Suzuki's book is a kaleidoscopic guided tour of the patented mathematical innovations that by and large now distinctly characterize the twenty-first century with respect to past eras.—Andrew James Simoson, MathSciNetTable of ContentsAcknowledgments Introduction. My Billion-Dollar Blunder Chapter 1: The Informational Hokey PokeyChapter 2: The Trillion-Dollar EquationChapter 3: A Picture Is a Thousand WordsChapter 4: If You Like Piña ColadasChapter 5: The Education RevolutionChapter 6: Forget Your Password? Forget Your Password!Chapter 7: The Company We KeepChapter 8: The Best of All Possible WorldsChapter 9: The Complete SagaChapter 10: Complexity from SimplicityChapter 11: RSA . . .Chapter 12: . . . Is PasséEpilogueBibliographyIndex

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Book SynopsisGain a deep understanding of data science and the thought process needed to solve problems in that field using the required techniques, technologies and skills that go into forming an interdisciplinary team. This book will enable you to set up an effective team of engineers, data scientists, analysts, and other stakeholders that can collaborate effectively on crucial aspects such as problem formulation, execution of experiments, and model performance evaluation. You'll start by delving into the fundamentals of data science classes of data science problems, data science techniques and their applications and gradually build up to building a professional reference operating model for a data science function in an organization. This operating model covers the roles and skills required in a team, the techniques and technologies they use, and the best practices typically followed in executing data science projects. Building an Effective Data Science Practiceprovides a common base of reTable of ContentsPart One: Fundamentals1. Introduction: The Data Science Process2. Data Science and your business 3. Monks vs. Cowboys: Data Science CulturesPart Two: Classes of Problems4. Classification 5. Regression6. Natural Language Processing 7. Clustering8. Anomaly Detection9. Recommendations10. Computer Vision11. Sequential Decision Making Part Three: Techniques & Technologies12. Overview13. Data Capture14. Data Preparation15. Data Visualization16. Machine Learning17. Inference18. Other tools and services19. Reference Architecture20. Monks vs. Cowboys: PraxisPart Four: Building Teams and Executing Projects21. The Skills Framework22. Building and structuring the team23. Data Science Projects Appendix FAQs

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Book SynopsisUnderstand modern data fabric and data mesh concepts using AI-based self-service data discovery and delivery capabilities, a range of intelligent data integration styles, and automated unified data governance-all designed to deliver data as a product within hybrid cloud landscapes.This book teaches you how to successfully deploy state-of-the-art data mesh solutions and gain a comprehensive overview on how a data fabric architecture uses artificial intelligence (AI) and machine learning (ML) for automated metadata management and self-service data discovery and consumption. You will learn how data fabric and data mesh relate to other concepts such as data DataOps, MLOps, AIDevOps, and more. Many examples are included to demonstrate how to modernize the consumption of data to enable a shopping-for-data (data as a product) experience.By the end of this book, you will understand the data fabric concept and architecture as it relates to themes such as automated unified dTable of ContentsPart I – Data Fabric FoundationChapter 1: Evolution of Data ArchitectureChapter 2: Terminology – Data Fabric and Data MeshChapter 3: Data Fabric and Data Mesh Use Case ScenariosChapter 4: Data Fabric and Data Mesh Business BenefitsPart II – Key Data Fabric Capabilities and ConceptsChapter 5: Key Data Fabric and Data Mesh CapabilitiesChapter 6: Relevant AI and ML ConceptsChapter 7: AI/ML for a Data Fabric and Data MeshChapter 8: AI for Entity ResolutionChapter 9: Data Fabric and Data Mesh for the AI LifecyclePart III – Deploying Data Fabric Solutions in ContextChapter 10: Data Fabric Architecture PatternsChapter 11: Role of Data Fabric within an Enterprise Architecture\Chapter 12: Data Fabric and Data Mesh in Hybrid Cloud LandscapeChapter 13: Intelligent Cataloging and Metadata ManagementChapter 14: Automated Data Fabric and Data Mesh AspectsChapter 15: Data Governance in the Context of Data Fabric and Data MeshPart IV – Current Offerings and Future AspectsChapter 16: Sample Vendor OfferingsChapter 17: Data Fabric and Data Mesh Research AreasChapter 18: In Summary and OnwardsAbbreviations.

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Book SynopsisThis book introduces a Responsible AI framework and guides you through processes to apply at each stage of the machine learning (ML) life cycle, from problem definition to deployment, to reduce and mitigate the risks and harms found in artificial intelligence (AI) technologies. AI offers the ability to solve many problems today if implemented correctly and responsibly. This book helps you avoid negative impacts that in some cases have caused loss of life and develop models that are fair, transparent, safe, secure, and robust. The approach in this book raises your awareness of the missteps that can lead to negative outcomes in AI technologies and provides a Responsible AI framework to deliver responsible and ethical results in ML. It begins with an examination of the foundational elements of responsibility, principles, and data. Next comes guidance on implementation addressing issues such as fairness, transparency, safety, privacy, and robustness. The book helps you think responsiblTable of ContentsIntroductionPart I. Foundation1. Responsibility2. AI Principles3. DataPart II. Implementation4. Responsible AI Framework5. Fairness6. Safety7. Humans in the Loop8. Transparency9. Privacy and RobustnessPart III. Ethical Considerations10. Ethics of AI and MLReferences

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Book SynopsisWith this practical guide, developers and data scientists will discover how graph analytics deliver value, whether they're used for building dynamic network models or forecasting real-world behavior.

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Book SynopsisDonald Knuth's influence in computer science ranges from the invention of methods for translating and defining programming languages to the creation of the TeX and METAFONT systems for desktop publishing. His award-winning textbooks have become classics; his scientific papers are widely referenced and stand as milestones of development over a wide range of topics. The present volume, which is the fourth in a series of his collected works, is devoted to an important subfield of Computer Science that Knuth founded in the 1960s and still considers his main life's work. This field, to which he gave the name Analysis of Algorithms, deals with quantitative studies of computer techniques, leading to methods for understanding and predicting the efficiency of computer programs. More than 30 of the papers that helped to shape this field are reprinted and updated in the present collection, together with historical material that has not previously been published.Table of Contents1. An almost linear recurrence; 2. The problem of compatible representatives; 3. The analysis of algorithms; 4. Mathematical analysis of algorithms; 5. The average height of planted plane trees; 6. An experiment in optimal sorting; 7. Shellsort with three increments; 8. The dangers of computer science theory; 9. Optimum measurement points for program frequency counts; 10. Ordered Hash tables; 11. Recurrence relations based on minimization; 12. Estimating the efficiency of backtrack programs; 13. An analysis of alpha-beta pruning; 14. Linear probing and graphs; 15. Activity in an interleaved memory; 16. Notes on generalized Dedekind sums; 17. Analysis of the subtractive algorithm for greatest common divisors; 18. Complexity results for bandwidth minimization; 19. Analysis of a simple factorization algorithm; 20. The complexity of nonuniform random number generation; 21. A trivial algorithm whose analysis isn't; 22. Evaluation of Porter's constant; 23. The expectant linearity of a simple equivalence algorithm; 24. Deletions that preserve randomness; 25. The average time for carry propogation; 26. A terminological proposal; 27. An analysis of optimum caching; 28. Optimal prepaging and font caching; 29. The distribution of continued fraction approximations; 30. The toilet paper problem; 31. A recurrence related to trees; 32. Stable husbands; 33. Postscript about NP-hard problems; 34. Nested satisfiability; 35. Textbook examples of recursion; 36. An exact analysis of stable allocation; 37. Big omicron and big omega and big theta.

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